class_docstr: 3-layer feed-forward neural networks with jumping connections
  Parameters
  -----------
  in_dim: int
      Input feature size.
  hid_dim: int
      Hidden feature size.
  
  Functions
  -----------
  forward(feat):
      feat: Tensor
          [N * D], input features
code: def __init__(self, in_dim, hid_dim):
      super(FeedforwardNetwork, self).__init__()
  
      self.block = Sequential(
          Linear(in_dim, hid_dim),
          ReLU(),
          Linear(hid_dim, hid_dim),
          ReLU(),
          Linear(hid_dim, hid_dim),
          ReLU(),
      )
  
      self.jump_con = Linear(in_dim, hid_dim)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import GINConv, NNConv, Set2Set
  from dgl.nn.pytorch.glob import SumPooling
  from torch.nn import BatchNorm1d, GRU, Linear, ModuleList, ReLU, Sequential
  from utils import global_global_loss_, local_global_loss_
label_desc: Use image node_id 1 to create a new FeedforwardNetwork object from inherited base classes: nn with example: obj = FeedforwardNetwork(in_dim, hid_dim)